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Experimentation of deep learning on the subjects of micro-expression spotting and recognition.
| Date | Stars |
|---|---|
| 2026-07-31 | 284 |
| 2026-08-06 | 284 |
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# New work available @ https://github.com/IcedDoggie/DSSN-MER # Code Updates and some notes: Very sorry for the late reply to most of the messages due to industrial work commitments and rushing deadlines. Apparently os.m was missing and I found it in my local code base. added in External Tools/ tvl1flow /. hope it helps and thank you. Also, hope that it benefits your research works :) # Micro-Expression-with-Deep-Learning Experimentation of deep learning on the subjects of micro-expression spotting and recognition. # Platforms and dependencies Ubuntu 16.04 Python 3.6 Keras 2.0.6 Opencv 3.1.0 pandas 0.19.2 CuDNN 5110. (Optional but recommended for deep learning) # Download files from this url (CASMEII with TIM applied are removed due to licensing, hence you need to apply the TIM and cropping yourself, the link below is access request for the database.) http://fu.psych.ac.cn/CASME/casme2-en.php TIM-related: TIM code can be downloaded below, https://www.oulu.fi/cmvs/node/33019 Note: Parameters are default except from TIM size Optical Flow Related: I added some script to extract optical flow features (in External_Tools/tvl1flow_3/), original repo are from below: https://github.com/Paul-Darius/ipol-matlab/tree/master/tvl1flow_3/Matlab Since LSTM is used, all the numbers of files have to be the same length. Currently the code does not work on CASMEII Raw. SMIC not tested. Shape predictor for Facial Landmarks extraction: dlib.net/files/shape_predictor_68_face_landmarks.dat.bz2 vgg-16 model pretrained on LFW dataset: https://drive.google.com/file/d/1F99D1U9rhaDHp4Re_ky_NjbdT7vmz0mr/view?usp=sharing ( Not available Anymore, but if anyone found an equivalent one, please do not hesitate to reach me out, thank you and sorry for the troubles.) Found one, try this https://github.com/rcmalli/keras-vggface ( if it does not work, let me know via email, thanks ) CASME2_Optical: https://drive.google.com/open?id=1fq_eHCLiUT9hP0npq6vkMYiO2Ka-39Mf CASME2_STRAIN: https://drive.google.com/open?id=1-l_CtP9awfMV6pXSrBIPRiIujQLjCv9H # Running from scratch main.py is a main control script to run the codes and there are several parameters to tune the training. The guide is as follows: **List of parameters**: --train: determines the training script to run. eg: train.py, train_samm_cross.py --batch_size: the number of data to be run per batch --spatial_epochs: the number of epochs to run for spatial module(vgg module) --temporal_epochs: the number of epochs to run for the LSTM/Recurrent module. --train_id: the name of the training. --dB: the database/databases to be used. --spatial_size: the image resolution --flag: the type of training to be run. can choose whether to perform Spatial Enrichment, Temporal Enrichment or train single module only --objective_flag: choose either objective labels or emotion labels. --tensorboard: choose to use tensorboard. Deprecated. **Type of flags**: st - spatial temporal. it's used to train single DB with both vgg-lstm. st4se - spatial temporal four channel spatial enrichment. optical flow + optical strain. st4te - spatial temporal four channel temporal enrichment. train both optical flow and optical strain with pre-trained weights and separately. st5se - flow + strain + grayscale raw image. st5te - flow, strain and grayscale train separately with vgg pre-trained weights. flags with cde behind indicates that to use composite database evaluation as proposed in MEGC 2018. **Deprecated/not supported flags**: s - spatial only. training vgg only. Remember to use --train './train_spatial_only.py' t - temporal only. training the lstm only. nofine - without finetuning. use the pre-trained weights directly **Type of scripts**: main.py - control scripts. train.py - training scripts for single db and cde. models.py - deep models utilities.py - various functions for preprocessing and data loading. list_databases.py - scripts to load databases and restructure data. train_samm_cross.py - hde training test_samm_c
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Read on GitHubWould you bet a product on this? Bounded 0–100 and slow moving.
matched fp:08984041338f7d88, llm:Repository description: 'Experimentation of deep learning on the subjects of micro-expression spotting and recognition.' Language: Python. Focus on micro-expression spotting and recognition (computer vision tasks).
matched fp:08984041338f7d88, llm:Repository description: 'Experimentation of deep learning on the subjects of micro-expression spotting and recognition.' Language: Python. Focus on micro-expression spotting and recognition (computer vision tasks).